Review of the European Inland Fisheries Advisory Commission (EIFAC) experiments on stock assessment in lakes

A series of international collaborative experiments carried out under the auspices of EIFAC sought to compare various sampling methods for stock abundance estimation in European inland waters. Four water bodies were used: the Evo lakes (Finland), Lake Konnevesi (Finland), Lake Constance (Germany) and LakeTegel (Germany). The experiments emphasized the difficulties in obtaining reliable fish stock estimates when using different sampling methods. Comparisons between the various methods were also limited. Poor data reliability was attributed both to problems inherent in the various methods and to deficiencies in the sampling design. These results emphasize the caution with which sampling data from inland water bodies should be interpreted. Suggestions are made by the EIFAC group for further studies.

Zusammenfassung

Überblick über die EIFAC-Experimente zur Bestandsabschätzung bei Seen
In internationaler Zusammenarbeit wurde eine Serie von Exerimenten auf Anregung der EIFAC durchgeführt. Dabei wurden Methoden der Fischbestandsabsciätzung in europäischen Binnengewässern verglichen. Die Experimente wurden an 4 Orten durchgeführt: den Evo-Seen und dem Konnevesisee in Finnland sowie dem Bodensee und dem Tegeler See in Deutschland. Es zeigte sich die Schwierigkeit, die Ergebnisse verschiedener Methoden zu vergleichen und zu einer befriedienden Bestandsschätzung zu kommen. Die Schwierigkeiten gehen auf die Methoden selbst wie auf Be Art ihres Einsatzes zurück. Die Ergebnisse der Experimente mahnen zur Vorsicht bei der Interpretation von Bestandsabschätzungen in Binnengewässern. Es werden Vorschläge für das kftige Vorgehen der EIFAC-Arbeitsgruppe und anderer Bearbeiter gemacht.     

Statistical analysis of microarray data: a Bayesian approach

The potential of microarray data is enormous. It allows us to monitor the expression of thousands of genes simultaneously. A common task with microarray is to determine which genes are differentially expressed between two samples obtained under two different conditions. Recently, several statistical methods have been proposed to perform such a task when there are replicate samples under each condition. Two major problems arise with microarray data. The first one is that the number of replicates is very small (usually 2-10), leading to noisy point estimates. As a consequence, traditional statistics that are based on the means and standard deviations, e.g. t-statistic, are not suitable. The second problem is that the number of genes is usually very large (approximately 10,000), and one is faced with an extreme multiple testing problem. Most multiple testing adjustments are relatively conservative, especially when the number of replicates is small. In this paper we present an empirical Bayes analysis that handles both problems very well. Using different parametrizations, we develop four statistics that can be used to test hypotheses about the means and/or variances of the gene expression levels in both one- and two-sample problems. The methods are illustrated using experimental data with prior knowledge. In addition, we present the result of a simulation comparing our methods to well-known statistics and multiple testing adjustments.     

Fisheries in the Southern Ocean: an ecosystem approach

The Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) is bound by its Article II, 3 to follow an ecosystem approach to management. This approach has been extended to the application of a precautionary approach in the late 1980s. In our review, we deal primarily with the science-related aspects of CCAMLR and its development towards an ecosystem approach to the management of the living resources of the Southern Ocean. To assist the Commission in meeting its objectives, as set out in Article II, 3, the Scientific Committee established the CCAMLR Ecosystem Monitoring Programme to detect possible effects of krill fishing on the performance of top-level predators, such as albatrosses, penguins, petrels and fur seals. Fisheries in the Southern Ocean followed the fate of other fisheries worldwide in which target species were depleted to low level one after the other. Currently, two types of fisheries are open: the longline fisheries on Patagonian toothfish (Dissostichus eleginoides) and Antarctic toothfish (Dissostichus mawsoni) and the trawl fisheries on mackerel icefish (Champsocephalus gunnari). Both fisheries are managed in a single-species context, however, with conservation measures in place to protect by-catch species, such as rattails (Macrouridae) and skates and rays (Rajidae). Two major problems still exist in fisheries in the Southern Ocean: the by-catch of birds in longline fisheries primarily in the Indian Ocean and the high level of IUU fishing again in the Indian Ocean. Both, the by-catch of birds and high IUU catches undermine the credibility of CCAMLR to safeguard the marine living resources in the Southern Ocean.     

Microfluorometry analysis II. A Bayesian approach.

In flow microfluorometry (FMF) analysis cells stained with a fluorescent dye that binds specifically to DNA are passed through the instrument. The number of cells in the population having a fluorescence intensity is recorded in a single channel of a multichannel pulse height analyzer. The result is a DNA fluorescence histogram for the population.A method for decomposing an FMF histogram into its G₁, S and G₂ + M components, corresponding to similarly designated phases of the cell cycle is given. This technique can also be applied to find the parameters in all of the previous approaches. The parameters are calculated by iteration which eliminates the need for non-linear optimization procedures.     

A decision-analytic framework for impact assessment part I: LCA and decision analysis

Life-cycle assessments (LCAs) are conducted to satisfy the aspiration of decision makers to consider the environment in their decision making. This paper reviews decision analysis and discusses how it can be used to structure the assessment and to integrate characterization and valuation. The decision analytic concepts of objectives (goals) and attributes (indicators of the degree to which an objective is achieved) are used to describe steps of the assessment of the entire impact chain. Decision analysis distinguishes among different types of objectives and attributes; it describes how these relate to each other. Impact indicators such as the Human Toxicity Potential are constructed attributes. A means-ends objectives network can show how the different constructed attributes relate to the objective of protecting the environment. As LCA takes disparate environmental impacts into account, it needs to assess their relative importance. Trade-off methods in decision analysis are grouped into utility theory and multicriteria decision aids; they have different advantages and disadvantages, but are all more sophisticated than simple weighting. The performance of the different trade-off methods has not yet been tested in an LCA context. In the second part of the paper, we present criteria for the development of characterization methods.     

BGX: a fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data

We present Bayesian hierarchical models for the analysis of Affymetrix GeneChip data. The approach we take differs from other available approaches in two fundamental aspects. Firstly, we aim to integrate all processing steps of the raw data in a common statistically coherent framework, allowing all components and thus associated errors to be considered simultaneously. Secondly, inference is based on the full posterior distribution of gene expression indices and derived quantities, such as fold changes or ranks, rather than on single point estimates. Measures of uncertainty on these quantities are thus available. The models presented represent the first building block for integrated Bayesian Analysis of Affymetrix GeneChip data: the models take into account additive as well as multiplicative error, gene expression levels are estimated using perfect match and a fraction of mismatch probes and are modeled on the log scale. Background correction is incorporated by modeling true signal and cross-hybridization explicitly, and a need for further normalization is considerably reduced by allowing for array-specific distributions of nonspecific hybridization. When replicate arrays are available for a condition, posterior distributions of condition-specific gene expression indices are estimated directly, by a simultaneous consideration of replicate probe sets, avoiding averaging over estimates obtained from individual replicate arrays. The performance of the Bayesian model is compared to that of standard available point estimate methods on subsets of the well known GeneLogic and Affymetrix spike-in data. The Bayesian model is found to perform well and the integrated procedure presented appears to hold considerable promise for further development.     

Hydrological attributes and rheophilic freshwater fish: stock assessment

The mathematical and statistical advances in fitting stock assessment models enabled the emergence of the paradigm of “integrated analysis”, which fits all available data to a single model of population dynamics that traditionally has total catch as the only forcing function of the system. This approach, however, allowed us to include, in a flexible way, the effect of hydrological regime as an additional forcing function. We tried to achieve this flexibility by making the annual recruitment rates and spawning biomass adjustable to the attributes of the hydrological cycle data. Our models showed that these attributes are influential in the population dynamics of Brycon hilarii of the Northern Pantanal, and their inclusion in the models allowed best partial fits (which considered fits only to the data components length- and age-compositions, CPUE of juveniles and adults) than the Base-case (without hydrological attributes). The best partial fits where obtained when the attributes “delay of floods” and “intensity of floods” were forcing the spawning biomass and the annual recruitment respectively, indicating that these characteristics of the population may be influenced by specific attributes of the water level. The use of integrated modeling contributed with the advancement of population ecology knowledge of rheophilic fish. It is recommended that freshwater fisheries management be integrated into the hydrology management.     

Population dynamics and potential of fisheries stock enhancement: practical theory for assessment and policy analysis

The population dynamics of fisheries stock enhancement, and its potential for generating benefits over and above those obtainable from optimal exploitation of wild stocks alone are poorly understood and highly controversial. I review pertinent knowledge of fish population biology, and extend the dynamic pool theory of fishing to stock enhancement by unpacking recruitment, incorporating regulation in the recruited stock, and accounting for biological differences between wild and hatchery fish. I then analyse the dynamics of stock enhancement and its potential role in fisheries management, using the candidate stock of North Sea sole as an example and considering economic as well as biological criteria. Enhancement through release of recruits or advanced juveniles is predicted to increase total yield and stock abundance, but reduce abundance of the naturally recruited stock component through compensatory responses or overfishing. Economic feasibility of enhancement is subject to strong constraints, including trade-offs between the costs of fishing and hatchery releases. Costs of hatchery fish strongly influence optimal policy, which may range from no enhancement at high cost to high levels of stocking and fishing effort at low cost. Release of genetically maladapted fish reduces the effectiveness of enhancement, and is most detrimental overall if fitness of hatchery fish is only moderately compromised. As a temporary measure for the rebuilding of depleted stocks, enhancement cannot substitute for effort limitation, and is advantageous as an auxiliary measure only if the population has been reduced to a very low proportion of its unexploited biomass. Quantitative analysis of population dynamics is central to the responsible use of stock enhancement in fisheries management, and the necessary tools are available.     

A Thurstonian model for quantitative genetic analysis of ranks: a Bayesian approach

A fully Bayesian method for quantitative genetic analysis of data consisting of ranks of, e.g., genotypes, scored at a series of events or experiments is presented. The model postulates a latent structure, with an underlying variable realized for each genotype or individual involved in the event. The rank observed is assumed to reflect the order of the values of the unobserved variables, i.e., the classical Thurstonian model of psychometrics. Parameters driving the Bayesian hierarchical model include effects of covariates, additive genetic effects, permanent environmental deviations, and components of variance. A Markov chain Monte Carlo implementation based on the Gibbs sampler is described, and procedures for inferring the probability of yet to be observed future rankings are outlined. Part of the model is rendered nonparametric by introducing a Dirichlet process prior for the distribution of permanent environmental effects. This can lead to potential identification of clusters of such effects, which, in some competitions such as horse races, may reflect forms of undeclared preferential treatment.     

A Bayesian approach for the analysis of panel-count data with dependent termination

We consider modeling and Bayesian analysis for panel-count data when the termination time for each subject may depend on its history of the recurrent events. We propose a fully specified semiparametric model for the joint distribution of the recurrent events and the termination time. For this model, we provide a natural motivation, derive several novel properties, and develop a Bayesian analysis based on a Markov chain Monte Carlo algorithm. Comparisons are made to other existing models and methods for panel-count data. We demonstrate the usefulness of our new models and methodologies through the reanalysis of a data set from a clinical trial.     

Refining gene signatures: a Bayesian approach

Background  

In high density arrays, the identification of relevant genes for disease classification is complicated by not only the curse of dimensionality but also the highly correlated nature of the array data. In this paper, we are interested in the question of how many and which genes should be selected for a disease class prediction. Our work consists of a Bayesian supervised statistical learning approach to refine gene signatures with a regularization which penalizes for the correlation between the variables selected.     

Catchability: a key parameter for fish stock assessment

Summary Catchability is a concept in fishery biology which reflects the efficiency of a particular fishery. Its quantitative magnitude is expressed by the catchability coefficient, which relates the biomass abundance to the capture or fishing mortality. This paper is a comprehensive review of catchability including the development of our knowledge, interpretation and estimation.Catchability patterns indicate that the catchability coefficient has been used in two main lines: (a) increased efficiency of fishing effort and (b) its relation to population fishery processes for assessment and management purposes. It involves various aspects of the fishery, such as individual and population biology, characteristics of the fishing gear, amount of fishing, fishing strategies, and environmental fluctuation, among others.The concept is proposed of an integrated model of the catchability coefficient, which incorporates various of the aspects mentioned above. It is illustrated with two examples of its application: the red grouper (Epinephelus morio) fishery from the Campeche Bank, Gulf of Mexico, and the sardine (Sardinops caeruleus) fishery from the Gulf of California.     

PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory

Background  

As a reversible and dynamic post-translational modification (PTM) of proteins, phosphorylation plays essential regulatory roles in a broad spectrum of the biological processes. Although many studies have been contributed on the molecular mechanism of phosphorylation dynamics, the intrinsic feature of substrates specificity is still elusive and remains to be delineated.     

Gene selection: a Bayesian variable selection approach

Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Bayesian mixture prior to perform the variable selection. We control the size of the model by assigning a prior distribution over the dimension (number of significant genes) of the model. The posterior distributions of the parameters are not in explicit form and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the parameters from the posteriors. The Bayesian model is flexible enough to identify significant genes as well as to perform future predictions. The method is applied to cancer classification via cDNA microarrays where the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify a set of significant genes. The method is also applied successfully to the leukemia data. SUPPLEMENTARY INFORMATION: http://stat.tamu.edu/people/faculty/bmallick.html.     

Combined linkage disequilibrium and linkage mapping: Bayesian multilocus approach

Quantitative trait loci (QTL) affecting the phenotype of interest can be detected using linkage analysis (LA), linkage disequilibrium (LD) mapping or a combination of both (LDLA). The LA approach uses information from recombination events within the observed pedigree and LD mapping from the historical recombinations within the unobserved pedigree. We propose the Bayesian variable selection approach for combined LDLA analysis for single-nucleotide polymorphism (SNP) data. The novel approach uses both sources of information simultaneously as is commonly done in plant and animal genetics, but it makes fewer assumptions about population demography than previous LDLA methods. This differs from approaches in human genetics, where LDLA methods use LA information conditional on LD information or the other way round. We argue that the multilocus LDLA model is more powerful for the detection of phenotype–genotype associations than single-locus LDLA analysis. To illustrate the performance of the Bayesian multilocus LDLA method, we analyzed simulation replicates based on real SNP genotype data from small three-generational CEPH families and compared the results with commonly used quantitative transmission disequilibrium test (QTDT). This paper is intended to be conceptual in the sense that it is not meant to be a practical method for analyzing high-density SNP data, which is more common. Our aim was to test whether this approach can function in principle.